RT Generic
T1 Self organizing maps for outlier detection
A1 Muñoz, Alberto
A1 Muruzábal, Jorge
A2 Universidad Carlos III de Madrid. Departamento de Estadística, 
AB In this paper we address the problem of multivariate outlier detection using the (unsupervised) self-organizing map (SOM) algorithm introduced by Kohonen. We examine a number of techniques, based on summary statistics and graphics derived from the trained SOM, and conclude that they work well in cooperation with each other. Useful tools include the median interneuron distance matrix and the projection ofthe trained map (via Sammon's projection). SOM quantization errors provide an important complementary source of information for certain type of outlying behavior. Empirical results are reported on both artificial and real data.
YR 1995
FD 1995-11
LK https://hdl.handle.net/10016/10345
UL https://hdl.handle.net/10016/10345
LA eng
DS e-Archivo
RD 5 jul. 2024